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"""
Power analysis tools for difference-in-differences study design.
This module provides power calculations and simulation-based power analysis
for DiD study design, helping practitioners answer questions like:
- "How many units do I need to detect an effect of size X?"
- "What is the minimum detectable effect given my sample size?"
- "What power do I have to detect a given effect?"
References
----------
Bloom, H. S. (1995). "Minimum Detectable Effects: A Simple Way to Report the
Statistical Power of Experimental Designs." Evaluation Review, 19(5), 547-556.
Burlig, F., Preonas, L., & Woerman, M. (2020). "Panel Data and Experimental Design."
Journal of Development Economics, 144, 102458.
Djimeu, E. W., & Houndolo, D.-G. (2016). "Power Calculation for Causal Inference
in Social Science: Sample Size and Minimum Detectable Effect Determination."
Journal of Development Effectiveness, 8(4), 508-527.
"""
import warnings
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
from scipy import stats
# Maximum sample size returned when effect is too small to detect
# (e.g., zero effect or extremely small relative to noise)
MAX_SAMPLE_SIZE = 2**31 - 1
# ---------------------------------------------------------------------------
# Estimator registry — maps estimator class names to DGP/fit/extract profiles
# ---------------------------------------------------------------------------
@dataclass
class _EstimatorProfile:
"""Internal profile describing how to run power simulations for an estimator."""
default_dgp: Callable
dgp_kwargs_builder: Callable
fit_kwargs_builder: Callable
result_extractor: Callable
min_n: int = 20
# ---------------------------------------------------------------------------
# SurveyPowerConfig — carries DGP survey params for simulation power
# ---------------------------------------------------------------------------
@dataclass
class SurveyPowerConfig:
"""Configuration for survey-aware power simulations.
When passed to :func:`simulate_power`, :func:`simulate_mde`, or
:func:`simulate_sample_size`, the simulation loop generates data with
:func:`~diff_diff.prep.generate_survey_did_data` and automatically
injects a ``SurveyDesign`` into the estimator's ``fit()`` call.
Parameters
----------
n_strata : int, default=5
Number of geographic strata.
psu_per_stratum : int, default=8
Number of primary sampling units (PSUs) per stratum. Must be >= 2
for Taylor Series Linearization variance estimation.
fpc_per_stratum : float, default=200.0
Finite population correction (total PSUs per stratum).
weight_variation : str, default="moderate"
Sampling weight dispersion: ``"none"`` (all equal), ``"moderate"``
(range ~1-2), ``"high"`` (range ~1-4).
psu_re_sd : float, default=2.0
Standard deviation of PSU random effects. Controls intra-cluster
correlation and drives DEFF > 1.
psu_period_factor : float, default=0.5
Multiplier for PSU-period interaction shocks.
icc : float, optional
Target intra-class correlation (0 < icc < 1). Overrides
``psu_re_sd`` via variance decomposition.
weight_cv : float, optional
Target coefficient of variation for weights. Overrides
``weight_variation``.
informative_sampling : bool, default=False
If True, weights correlate with Y(0).
heterogeneous_te_by_strata : bool, default=False
If True, treatment effect varies by stratum.
include_replicate_weights : bool, default=False
If True, add JK1 delete-one-PSU replicate weight columns.
survey_design : SurveyDesign, optional
Override the auto-built SurveyDesign. When None, a default
``SurveyDesign(weights="weight", strata="stratum", psu="psu",
fpc="fpc")`` is used, matching ``generate_survey_did_data`` output.
Examples
--------
>>> from diff_diff import CallawaySantAnna, simulate_power, SurveyPowerConfig
>>> config = SurveyPowerConfig(n_strata=5, psu_per_stratum=8, icc=0.05)
>>> results = simulate_power(
... CallawaySantAnna(),
... n_units=200,
... treatment_effect=2.0,
... survey_config=config,
... n_simulations=100,
... seed=42,
... )
"""
n_strata: int = 5
psu_per_stratum: int = 8
fpc_per_stratum: float = 200.0
weight_variation: str = "moderate"
psu_re_sd: float = 2.0
psu_period_factor: float = 0.5
icc: Optional[float] = None
weight_cv: Optional[float] = None
informative_sampling: bool = False
heterogeneous_te_by_strata: bool = False
include_replicate_weights: bool = False
survey_design: Optional[Any] = None
def __post_init__(self) -> None:
if self.n_strata < 1:
raise ValueError(f"n_strata must be >= 1, got {self.n_strata}")
if self.psu_per_stratum < 2:
raise ValueError(
f"psu_per_stratum must be >= 2 for TSL variance estimation, "
f"got {self.psu_per_stratum}"
)
if self.weight_variation not in ("none", "moderate", "high"):
raise ValueError(
f"weight_variation must be 'none', 'moderate', or 'high', "
f"got '{self.weight_variation}'"
)
if not np.isfinite(self.psu_re_sd) or self.psu_re_sd < 0:
raise ValueError(f"psu_re_sd must be finite and >= 0, got {self.psu_re_sd}")
if not np.isfinite(self.fpc_per_stratum):
raise ValueError(f"fpc_per_stratum must be finite, got {self.fpc_per_stratum}")
if self.icc is not None and not (0 < self.icc < 1):
raise ValueError(f"icc must be between 0 and 1 (exclusive), got {self.icc}")
if self.icc is not None and self.psu_re_sd != 2.0:
raise ValueError(
"Cannot specify both icc and a non-default psu_re_sd. "
"icc overrides psu_re_sd via the ICC formula."
)
if self.weight_cv is not None:
if not np.isfinite(self.weight_cv) or self.weight_cv <= 0:
raise ValueError(f"weight_cv must be finite and > 0, got {self.weight_cv}")
if self.weight_variation != "moderate":
raise ValueError(
"Cannot specify both weight_cv and a non-default "
"weight_variation. weight_cv overrides weight_variation."
)
if not np.isfinite(self.psu_period_factor) or self.psu_period_factor < 0:
raise ValueError(
f"psu_period_factor must be finite and >= 0, got {self.psu_period_factor}"
)
if self.fpc_per_stratum < self.psu_per_stratum:
raise ValueError(
f"fpc_per_stratum ({self.fpc_per_stratum}) must be >= "
f"psu_per_stratum ({self.psu_per_stratum})"
)
def _build_survey_design(self) -> Any:
"""Return a SurveyDesign for this config.
Reflects the live ``self.survey_design`` value every call (no
caching). Finding #28 (axis J, silent-failures audit): the
previous ``_cached_survey_design`` was populated on first call
and never invalidated on mutation, so ``config.survey_design =
other_design`` silently kept returning the original. Since the
default ``SurveyDesign(...)`` construction is microseconds and
user-provided designs are just reference copies, there's no cache
cost worth keeping.
"""
if self.survey_design is not None:
return self.survey_design
from diff_diff.survey import SurveyDesign
return SurveyDesign(
weights="weight", strata="stratum", psu="psu", fpc="fpc"
)
@property
def min_viable_n(self) -> int:
"""Minimum n_units for a viable survey design (>= 2 units per PSU)."""
return self.n_strata * self.psu_per_stratum * 2
# -- DGP kwargs adapters -----------------------------------------------------
def _basic_dgp_kwargs(
n_units: int,
n_periods: int,
treatment_effect: float,
treatment_fraction: float,
treatment_period: int,
sigma: float,
) -> Dict[str, Any]:
return dict(
n_units=n_units,
n_periods=n_periods,
treatment_effect=treatment_effect,
treatment_fraction=treatment_fraction,
treatment_period=treatment_period,
noise_sd=sigma,
)
def _staggered_dgp_kwargs(
n_units: int,
n_periods: int,
treatment_effect: float,
treatment_fraction: float,
treatment_period: int,
sigma: float,
) -> Dict[str, Any]:
return dict(
n_units=n_units,
n_periods=n_periods,
treatment_effect=treatment_effect,
never_treated_frac=1 - treatment_fraction,
cohort_periods=[treatment_period],
dynamic_effects=False,
noise_sd=sigma,
)
def _factor_dgp_kwargs(
n_units: int,
n_periods: int,
treatment_effect: float,
treatment_fraction: float,
treatment_period: int,
sigma: float,
) -> Dict[str, Any]:
n_pre = treatment_period
n_post = n_periods - treatment_period
return dict(
n_units=n_units,
n_pre=n_pre,
n_post=n_post,
n_treated=max(1, int(n_units * treatment_fraction)),
treatment_effect=treatment_effect,
noise_sd=sigma,
)
def _ddd_dgp_kwargs(
n_units: int,
n_periods: int,
treatment_effect: float,
treatment_fraction: float,
treatment_period: int,
sigma: float,
) -> Dict[str, Any]:
return dict(
n_per_cell=max(2, n_units // 8),
treatment_effect=treatment_effect,
noise_sd=sigma,
)
# -- Fit kwargs builders ------------------------------------------------------
def _basic_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
) -> Dict[str, Any]:
return dict(outcome="outcome", treatment="treated", time="post")
def _twfe_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
) -> Dict[str, Any]:
return dict(outcome="outcome", treatment="treated", time="post", unit="unit")
def _multiperiod_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
) -> Dict[str, Any]:
return dict(
outcome="outcome",
treatment="treated",
time="period",
post_periods=list(range(treatment_period, n_periods)),
)
def _staggered_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
) -> Dict[str, Any]:
return dict(outcome="outcome", unit="unit", time="period", first_treat="first_treat")
def _ddd_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
) -> Dict[str, Any]:
return dict(outcome="outcome", group="group", partition="partition", time="time")
def _trop_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
) -> Dict[str, Any]:
return dict(outcome="outcome", treatment="treated", unit="unit", time="period")
def _sdid_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
) -> Dict[str, Any]:
periods = sorted(data["period"].unique())
post_periods = [p for p in periods if p >= treatment_period]
return dict(
outcome="outcome",
treatment="treat",
unit="unit",
time="period",
post_periods=post_periods,
)
# -- Survey-aware DGP kwargs adapter ------------------------------------------
def _survey_dgp_kwargs(
n_units: int,
n_periods: int,
treatment_effect: float,
treatment_fraction: float,
treatment_period: int,
sigma: float,
survey_config: SurveyPowerConfig,
) -> Dict[str, Any]:
"""Build kwargs for generate_survey_did_data from simulate_power params."""
return dict(
n_units=n_units,
n_periods=n_periods,
treatment_effect=treatment_effect,
never_treated_frac=1 - treatment_fraction,
# 0-indexed treatment_period → 1-indexed cohort_periods
cohort_periods=[treatment_period + 1],
noise_sd=sigma,
dynamic_effects=False,
n_strata=survey_config.n_strata,
psu_per_stratum=survey_config.psu_per_stratum,
fpc_per_stratum=survey_config.fpc_per_stratum,
weight_variation=survey_config.weight_variation,
psu_re_sd=survey_config.psu_re_sd,
psu_period_factor=survey_config.psu_period_factor,
icc=survey_config.icc,
weight_cv=survey_config.weight_cv,
informative_sampling=survey_config.informative_sampling,
heterogeneous_te_by_strata=survey_config.heterogeneous_te_by_strata,
include_replicate_weights=survey_config.include_replicate_weights,
return_true_population_att=True,
)
# -- Survey-aware fit kwargs builders -----------------------------------------
def _survey_basic_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
survey_config: SurveyPowerConfig,
) -> Dict[str, Any]:
"""Fit kwargs for DifferenceInDifferences with survey design.
Uses ``ever_treated`` (time-invariant group indicator) rather than the
survey DGP's ``treated`` column (which is post-only: 1{g>0, t>=g}).
DifferenceInDifferences internally constructs ``treatment * time``,
so passing the post-only flag would make that interaction rank-deficient.
"""
return dict(
outcome="outcome",
treatment="ever_treated",
time="post",
survey_design=survey_config._build_survey_design(),
)
def _survey_twfe_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
survey_config: SurveyPowerConfig,
) -> Dict[str, Any]:
"""Fit kwargs for TwoWayFixedEffects with survey design."""
return dict(
outcome="outcome",
treatment="ever_treated",
time="post",
unit="unit",
survey_design=survey_config._build_survey_design(),
)
def _survey_multiperiod_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
survey_config: SurveyPowerConfig,
) -> Dict[str, Any]:
"""Fit kwargs for MultiPeriodDiD with survey design (1-indexed periods)."""
return dict(
outcome="outcome",
treatment="ever_treated",
unit="unit",
time="period",
# 1-indexed: post periods run from treatment_period+1 to n_periods
post_periods=list(range(treatment_period + 1, n_periods + 1)),
survey_design=survey_config._build_survey_design(),
)
def _survey_staggered_fit_kwargs(
data: pd.DataFrame,
n_units: int,
n_periods: int,
treatment_period: int,
survey_config: SurveyPowerConfig,
) -> Dict[str, Any]:
"""Fit kwargs for staggered estimators (CS, SA, etc.) with survey design."""
return dict(
outcome="outcome",
unit="unit",
time="period",
first_treat="first_treat",
survey_design=survey_config._build_survey_design(),
)
# -- Result extractors --------------------------------------------------------
def _extract_simple(result: Any) -> Tuple[float, float, float, Tuple[float, float]]:
return (result.att, result.se, result.p_value, result.conf_int)
def _extract_multiperiod(
result: Any,
) -> Tuple[float, float, float, Tuple[float, float]]:
return (result.avg_att, result.avg_se, result.avg_p_value, result.avg_conf_int)
def _extract_staggered(
result: Any,
) -> Tuple[float, float, float, Tuple[float, float]]:
_nan = float("nan")
_nan_ci = (_nan, _nan)
def _first(r: Any, *attrs: str, default: Any = _nan) -> Any:
for a in attrs:
v = getattr(r, a, None)
if v is not None:
return v
return default
return (
result.overall_att,
_first(result, "overall_se", "overall_att_se"),
_first(result, "overall_p_value", "overall_att_p_value"),
_first(result, "overall_conf_int", "overall_att_ci", default=_nan_ci),
)
# Keys derived from simulate_power() public params — overriding these
# via data_generator_kwargs would desync the DGP from the result object.
_PROTECTED_DGP_KEYS = frozenset(
{
"treatment_effect", # → true_effect in results / MDE search variable
"noise_sd", # → sigma param
"n_units", # → sample-size search variable
"n_periods", # → n_periods param
"treatment_fraction", # → treatment_fraction param
"treatment_period", # → treatment_period param
"n_pre", # → derived from treatment_period in factor-model DGPs
"n_post", # → derived from n_periods - treatment_period in factor-model DGPs
}
)
# Keys managed by SurveyPowerConfig — block in data_generator_kwargs when
# survey_config is active to prevent silent conflicts.
_SURVEY_CONFIG_KEYS = frozenset(
{
"n_strata",
"psu_per_stratum",
"fpc_per_stratum",
"weight_variation",
"psu_re_sd",
"psu_period_factor",
"icc",
"weight_cv",
"informative_sampling",
"heterogeneous_te_by_strata",
"include_replicate_weights",
"return_true_population_att",
"dynamic_effects",
"cohort_periods",
"never_treated_frac",
}
)
# -- Staggered DGP compatibility check ----------------------------------------
_STAGGERED_ESTIMATORS = frozenset(
{
"CallawaySantAnna",
"SunAbraham",
"ImputationDiD",
"TwoStageDiD",
"StackedDiD",
"EfficientDiD",
}
)
# Estimators that need a derived `post` column when using survey DGP
# (survey DGP produces `period`/`first_treat` but not `post`).
_SURVEY_POST_ESTIMATORS = frozenset({"DifferenceInDifferences", "TwoWayFixedEffects"})
# Survey fit kwargs builder lookup — maps estimator name to builder function.
_SURVEY_FIT_BUILDERS: Dict[str, Callable] = {
"DifferenceInDifferences": _survey_basic_fit_kwargs,
"TwoWayFixedEffects": _survey_twfe_fit_kwargs,
"MultiPeriodDiD": _survey_multiperiod_fit_kwargs,
**{name: _survey_staggered_fit_kwargs for name in _STAGGERED_ESTIMATORS},
}
# Unsupported: factor-model and triple-diff estimators (survey DGP produces
# staggered cohort data, not factor-model or 2x2x2 data).
_SURVEY_UNSUPPORTED = frozenset({"TROP", "SyntheticDiD", "TripleDifference"})
def _check_staggered_dgp_compat(
estimator: Any,
data_generator_kwargs: Optional[Dict[str, Any]],
) -> None:
"""Warn if a staggered estimator's settings don't match the default DGP."""
name = type(estimator).__name__
if name not in _STAGGERED_ESTIMATORS:
return
dgp_overrides = data_generator_kwargs or {}
cohort_periods = dgp_overrides.get("cohort_periods")
has_multi_cohort = cohort_periods is not None and len(set(cohort_periods)) >= 2
issues: List[str] = []
# Check control_group="not_yet_treated" (CS, SA)
cg = getattr(estimator, "control_group", "never_treated")
if cg == "not_yet_treated" and not has_multi_cohort:
issues.append(
f' - {name} has control_group="not_yet_treated" but the default '
f"DGP generates a single treatment cohort with never-treated "
f"controls. Power may not reflect the intended not-yet-treated "
f"design.\n"
f" Fix: pass data_generator_kwargs="
f'{{"cohort_periods": [2, 4], "never_treated_frac": 0.0}} '
f"(or a custom data_generator)."
)
# Check anticipation > 0 (all staggered)
antic = getattr(estimator, "anticipation", 0)
if antic > 0:
issues.append(
f" - {name} has anticipation={antic} but the default DGP does "
f"not model anticipatory effects. The estimator will look for "
f"treatment effects {antic} period(s) before the DGP generates "
f"them, biasing power estimates.\n"
f" Fix: supply a custom data_generator that shifts the "
f"effect onset."
)
# Check clean_control on StackedDiD
if name == "StackedDiD":
cc = getattr(estimator, "clean_control", "not_yet_treated")
if cc == "strict" and not has_multi_cohort:
issues.append(
' - StackedDiD has clean_control="strict" but the default '
"single-cohort DGP makes strict controls equivalent to "
"never-treated controls.\n"
" Fix: pass data_generator_kwargs="
'{"cohort_periods": [2, 4]} '
"to test true strict clean-control behavior."
)
if issues:
msg = (
f"Staggered power DGP mismatch for {name}. The default "
f"single-cohort DGP may not match the estimator "
f"configuration:\n" + "\n".join(issues)
)
warnings.warn(msg, UserWarning, stacklevel=2)
def _ddd_effective_n(
n_units: int, data_generator_kwargs: Optional[Dict[str, Any]]
) -> Optional[int]:
"""Return effective DDD sample size, or None if no rounding occurred."""
overrides = data_generator_kwargs or {}
if "n_per_cell" in overrides:
eff = overrides["n_per_cell"] * 8
else:
eff = max(2, n_units // 8) * 8
return eff if eff != n_units else None
def _check_ddd_dgp_compat(
n_units: int,
n_periods: int,
treatment_fraction: float,
treatment_period: int,
data_generator_kwargs: Optional[Dict[str, Any]],
) -> None:
"""Warn when simulation inputs don't match DDD's fixed 2×2×2 design."""
issues: List[str] = []
# DDD is a fixed 2-period factorial; n_periods and treatment_period are ignored
if n_periods != 2:
issues.append(
f"n_periods={n_periods} is ignored (DDD uses a fixed " f"2-period design: pre/post)"
)
if treatment_period != 1:
issues.append(
f"treatment_period={treatment_period} is ignored (DDD "
f"always treats in the second period)"
)
# DDD's 2×2×2 factorial has inherent 50% treatment fraction
if treatment_fraction != 0.5:
issues.append(
f"treatment_fraction={treatment_fraction} is ignored "
f"(DDD uses a balanced 2×2×2 factorial where 50% of "
f"groups are treated)"
)
# n_units rounding: n_per_cell = max(2, n_units // 8)
eff_n = _ddd_effective_n(n_units, data_generator_kwargs)
if eff_n is not None:
eff_n_per_cell = eff_n // 8
issues.append(
f"effective sample size is {eff_n} "
f"(n_per_cell={eff_n_per_cell} × 8 cells), "
f"not the requested n_units={n_units}"
)
if issues:
warnings.warn(
"TripleDifference uses a fixed 2×2×2 factorial DGP "
"(group × partition × time). "
+ "; ".join(issues)
+ ". Pass a custom data_generator for non-standard DDD designs.",
UserWarning,
stacklevel=2,
)
def _check_sdid_placebo_data(
data: pd.DataFrame,
estimator: Any,
est_kwargs: Dict[str, Any],
) -> None:
"""Check SyntheticDiD placebo feasibility on realized data.
This catches infeasible designs on the custom-DGP path where the
pre-generation check (which uses ``n_units * treatment_fraction``)
cannot run because treatment allocation is determined by the DGP.
"""
vm = getattr(estimator, "variance_method", "placebo")
if vm != "placebo":
return
treat_col = est_kwargs.get("treatment", "treat")
unit_col = est_kwargs.get("unit", "unit")
if treat_col not in data.columns or unit_col not in data.columns:
return # fit will fail with a more specific error
unit_treat = data.groupby(unit_col)[treat_col].first()
n_treated = int(unit_treat.sum())
n_control = len(unit_treat) - n_treated
if n_control <= n_treated:
raise ValueError(
f"SyntheticDiD placebo variance requires more control than "
f"treated units, but the generated data has n_control={n_control}, "
f"n_treated={n_treated}. Either adjust your data_generator so that "
f"n_control > n_treated, or use "
f"SyntheticDiD(variance_method='bootstrap') (paper-faithful refit; "
f"~5-30x slower than placebo) or SyntheticDiD(variance_method='jackknife')."
)
# -- Registry construction (deferred to avoid import-time cost) ---------------
_ESTIMATOR_REGISTRY: Optional[Dict[str, _EstimatorProfile]] = None
def _get_registry() -> Dict[str, _EstimatorProfile]:
"""Lazily build and return the estimator registry."""
global _ESTIMATOR_REGISTRY # noqa: PLW0603
if _ESTIMATOR_REGISTRY is not None:
return _ESTIMATOR_REGISTRY
from diff_diff.prep import (
generate_ddd_data,
generate_did_data,
generate_factor_data,
generate_staggered_data,
)
_ESTIMATOR_REGISTRY = {
# --- Basic DiD group ---
"DifferenceInDifferences": _EstimatorProfile(
default_dgp=generate_did_data,
dgp_kwargs_builder=_basic_dgp_kwargs,
fit_kwargs_builder=_basic_fit_kwargs,
result_extractor=_extract_simple,
min_n=20,
),
"TwoWayFixedEffects": _EstimatorProfile(
default_dgp=generate_did_data,
dgp_kwargs_builder=_basic_dgp_kwargs,
fit_kwargs_builder=_twfe_fit_kwargs,
result_extractor=_extract_simple,
min_n=20,
),
"MultiPeriodDiD": _EstimatorProfile(
default_dgp=generate_did_data,
dgp_kwargs_builder=_basic_dgp_kwargs,
fit_kwargs_builder=_multiperiod_fit_kwargs,
result_extractor=_extract_multiperiod,
min_n=20,
),
# --- Staggered group ---
"CallawaySantAnna": _EstimatorProfile(
default_dgp=generate_staggered_data,
dgp_kwargs_builder=_staggered_dgp_kwargs,
fit_kwargs_builder=_staggered_fit_kwargs,
result_extractor=_extract_staggered,
min_n=40,
),
"SunAbraham": _EstimatorProfile(
default_dgp=generate_staggered_data,
dgp_kwargs_builder=_staggered_dgp_kwargs,
fit_kwargs_builder=_staggered_fit_kwargs,
result_extractor=_extract_staggered,
min_n=40,
),
"ImputationDiD": _EstimatorProfile(
default_dgp=generate_staggered_data,
dgp_kwargs_builder=_staggered_dgp_kwargs,
fit_kwargs_builder=_staggered_fit_kwargs,
result_extractor=_extract_staggered,
min_n=40,
),
"TwoStageDiD": _EstimatorProfile(
default_dgp=generate_staggered_data,
dgp_kwargs_builder=_staggered_dgp_kwargs,
fit_kwargs_builder=_staggered_fit_kwargs,
result_extractor=_extract_staggered,
min_n=40,
),
"StackedDiD": _EstimatorProfile(
default_dgp=generate_staggered_data,
dgp_kwargs_builder=_staggered_dgp_kwargs,
fit_kwargs_builder=_staggered_fit_kwargs,
result_extractor=_extract_staggered,
min_n=40,
),
"EfficientDiD": _EstimatorProfile(
default_dgp=generate_staggered_data,
dgp_kwargs_builder=_staggered_dgp_kwargs,
fit_kwargs_builder=_staggered_fit_kwargs,
result_extractor=_extract_staggered,
min_n=40,
),
# --- Factor model group ---
"TROP": _EstimatorProfile(
default_dgp=generate_factor_data,
dgp_kwargs_builder=_factor_dgp_kwargs,
fit_kwargs_builder=_trop_fit_kwargs,
result_extractor=_extract_simple,
min_n=30,
),
"SyntheticDiD": _EstimatorProfile(
default_dgp=generate_factor_data,
dgp_kwargs_builder=_factor_dgp_kwargs,
fit_kwargs_builder=_sdid_fit_kwargs,
result_extractor=_extract_simple,
min_n=30,
),
# --- Triple difference ---
"TripleDifference": _EstimatorProfile(
default_dgp=generate_ddd_data,
dgp_kwargs_builder=_ddd_dgp_kwargs,
fit_kwargs_builder=_ddd_fit_kwargs,
result_extractor=_extract_simple,
min_n=64,
),
}
return _ESTIMATOR_REGISTRY
@dataclass
class PowerResults:
"""
Results from analytical power analysis.
Attributes
----------
power : float
Statistical power (probability of rejecting H0 when effect exists).
mde : float
Minimum detectable effect size.
required_n : int
Required total sample size (treated + control).
effect_size : float
Effect size used in calculation.
alpha : float
Significance level.
alternative : str
Alternative hypothesis ('two-sided', 'greater', 'less').
n_treated : int
Number of treated units.
n_control : int
Number of control units.
n_pre : int
Number of pre-treatment periods.
n_post : int
Number of post-treatment periods.
sigma : float
Residual standard deviation.
rho : float
Intra-cluster correlation (for panel data).
deff : float
Survey design effect (variance inflation factor).
design : str
Study design type ('basic_did', 'panel', 'staggered').
"""
power: float
mde: float
required_n: int
effect_size: float
alpha: float
alternative: str
n_treated: int
n_control: int
n_pre: int
n_post: int
sigma: float
rho: float = 0.0
deff: float = 1.0
design: str = "basic_did"
def __repr__(self) -> str:
"""Concise string representation."""
return (
f"PowerResults(power={self.power:.3f}, mde={self.mde:.4f}, "
f"required_n={self.required_n})"
)
def summary(self) -> str:
"""
Generate a formatted summary of power analysis results.
Returns
-------
str
Formatted summary table.
"""
lines = [
"=" * 60,
"Power Analysis for Difference-in-Differences".center(60),
"=" * 60,
"",
f"{'Design:':<30} {self.design}",
f"{'Significance level (alpha):':<30} {self.alpha:.3f}",
f"{'Alternative hypothesis:':<30} {self.alternative}",
"",
"-" * 60,
"Sample Size".center(60),
"-" * 60,
f"{'Treated units:':<30} {self.n_treated:>10}",
f"{'Control units:':<30} {self.n_control:>10}",
f"{'Total units:':<30} {self.n_treated + self.n_control:>10}",
f"{'Pre-treatment periods:':<30} {self.n_pre:>10}",
f"{'Post-treatment periods:':<30} {self.n_post:>10}",
"",
"-" * 60,
"Variance Parameters".center(60),
"-" * 60,
f"{'Residual SD (sigma):':<30} {self.sigma:>10.4f}",
f"{'Intra-cluster correlation:':<30} {self.rho:>10.4f}",
*([f"{'Design effect (DEFF):':<30} {self.deff:>10.4f}"] if self.deff != 1.0 else []),
"",
"-" * 60,
"Power Analysis Results".center(60),
"-" * 60,
f"{'Effect size:':<30} {self.effect_size:>10.4f}",
f"{'Power:':<30} {self.power:>10.1%}",
f"{'Minimum detectable effect:':<30} {self.mde:>10.4f}",
f"{'Required sample size:':<30} {self.required_n:>10}",
"=" * 60,
]
return "\n".join(lines)
def print_summary(self) -> None:
"""Print the summary to stdout."""
print(self.summary())
def to_dict(self) -> Dict[str, Any]:
"""
Convert results to a dictionary.
Returns
-------
Dict[str, Any]
Dictionary containing all power analysis results.
"""
return {
"power": self.power,
"mde": self.mde,
"required_n": self.required_n,
"effect_size": self.effect_size,
"alpha": self.alpha,
"alternative": self.alternative,
"n_treated": self.n_treated,
"n_control": self.n_control,
"n_pre": self.n_pre,
"n_post": self.n_post,
"sigma": self.sigma,
"rho": self.rho,
"deff": self.deff,
"design": self.design,
}
def to_dataframe(self) -> pd.DataFrame:
"""
Convert results to a pandas DataFrame.
Returns
-------
pd.DataFrame
DataFrame with power analysis results.
"""
return pd.DataFrame([self.to_dict()])
@dataclass
class SimulationPowerResults:
"""
Results from simulation-based power analysis.
Attributes
----------
power : float
Estimated power (proportion of simulations rejecting H0).
power_se : float
Standard error of power estimate.
power_ci : Tuple[float, float]
Confidence interval for power estimate.
rejection_rate : float
Proportion of simulations with p-value < alpha.
mean_estimate : float
Mean treatment effect estimate across simulations.
std_estimate : float
Standard deviation of estimates across simulations.
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